Near-Optimal Evasion of Convex-Inducing Classifiers
Blaine A. Nelson, Benjamin I. P. Rubinstein, Ling Huang, Anthony Douglas Joseph, Shing-hon Lau, Steven J. Lee, Satish B. Rao, Tran, Anthony, J. D. Tygar · arXiv (Cornell University) · 2010
Classifiers are often used to detect miscreant activities. We study how an adversary can efficiently query a classifier to elicit information that allows the adversary to evade detection at near-minimal cost. We generalize results of Lowd and Meek (2005) to convex-inducing classifiers. We present algorithms that construct undetected instances of near-minimal cost using only polynomially many queries in the dimension of the space and without reverse engineering the decision boundary.